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Affordably tracking the transmission of respiratory infectious diseases in urban transport infrastructures can inform individuals about potential exposure to diseases and guide public policymakers to prepare timely responses based on geographical transmission in different areas in the city. Towards that end, we designed and tested a method to detect SARS-CoV-2 RNA in the air filters of public buses, revealing that air filters could be used as passive fabric sensors for the detection of viral presence. We placed and retrieved filters in the existing HVAC systems of public buses to test for the presence of trapped SARS-CoV-2 RNA using phenol-chloroform extraction and RT-qPCR. SARS-CoV-2 RNA was detected in 14% (5/37) of public bus filters tested in Seattle, Washington, from August 2020 to March 2021. These results indicate that this sensing system is feasible and that, if scaled, this method could provide a unique lens into the geographically relevant transmission of SARS-CoV-2 through public transit rider vectors, pooling samples of riders over time in a passive manner without installing any additional systems on transit vehicles.

This paper introduces a novel technique to measure indoor ambient air temperature using the battery temperature sensor found on typical smartphones. We develop physics-based models to predict ambient air temperature that consider the many warming and cooling scenarios faced by phones and account for the excess heat generated by smartphone components such as the CPU, screen, network, and charging hardware. To accommodate never-beforeseen devices, we also develop a domain adaptation technique that leverages previously derived models, substantially reducing the overhead of learning accurate models for a new phone. We evaluate our models for a range of devices, operating scenarios, ambient temperatures, and phone cases, with mean errors generally less than 1.5% of ambient temperature. We also present a case study to demonstrate the utility of our approach for spatial and temporal monitoring of ambient temperature variations in an office building; while indoor conditions vary by as much as 13°F, mean error in measurement by our models is 1.4%.

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Smartphones contain thermistors that ordinarily monitor the temperature of the device's internal components; however, these sensors are also sensitive to warm entities in contact with the device, presenting opportunities for measuring human body temperature and detecting fevers. We present FeverPhone --- a smartphone app that estimates a person's core body temperature by having the user place the capacitive touchscreen of the phone against their forehead. During the assessment, the phone logs the temperature sensed by a thermistor and the raw capacitance sensed by the touchscreen to capture features describing the rate of heat transfer from the body to the device. These features are then used in a machine learning model to infer the user's core body temperature. We validate FeverPhone through both a lab simulation with a skin-like controllable heat source and a clinical study with real patients. We found that FeverPhone's temperature estimates are comparable to commercial off-the-shelf peripheral and tympanic thermometers. In a clinical study with 37 participants, FeverPhone readings achieved a mean absolute error of 0.229 °C, a limit of agreement of ±0.731 °C, and a Pearson's correlation coefficient of 0.763. Using these results for fever classification results in a sensitivity of 0.813 and a specificity of 0.904.